Take your PySpark machine learning skills to the next level by learning how to apply and evaluate predictive models for scalable data analytics. This intermediate-level course is designed for learners with Python knowledge and a foundation in machine learning who want to build, assess, and interpret machine learning models using Apache PySpark and MLlib.

You will begin by constructing linear regression models before progressing to Generalized Linear Regression, Random Forest Regression, and logistic regression for binary classification. Next, you will explore multinomial logistic regression, decision tree classifiers, Random Forest classification, and K-Means clustering for unsupervised learning. Throughout the course, you will reinforce each concept with guided PySpark code demonstrations, predictive workflows, model evaluation techniques, and practical analysis using large datasets. By the end of the course, you will be able to design, execute, and evaluate regression, classification, and clustering models in PySpark while interpreting model performance using appropriate evaluation methods. If you are looking to strengthen your ability to build scalable machine learning workflows in distributed environments, this course provides practical experience with widely used predictive modeling techniques in PySpark.